mie2012 27 aug12-shublaq

Personalised medicine: A legacy of promises without delivery. Can we get it right today?

Nour Shublaq

Centre for Computa-onal Science (CCS) University College London, UK

[email protected]

MIE 2012 – Process, Information, and Data Models, Monday Aug 27, 2012, Pisa

Overview

•  The Human Genome Project

•  The Virtual Physiological Human (VPH) ini-a-ve

•  VPH Simula-on Case Studies – 1) clinical decision support in surgery 2) towards personalised drug design

•  INBIOMEDvision – challenges ahead

•  EU FET Flagship project IT Future of Medicine

•  Conclusions

Human Genome Project

Sequencing of the human genome was profoundly important science that led to fundamental shifts in our understanding of biology.

30,000 – 40,000 protein coding genes in the human genome and not more than 100,000 previously thought.

Thousands of DNA variants have now been associated with traits/diseases.

Human Genome Project, International HapMap Project, and Genome wide association studies (GWAS) in the last decade

Structure Mol. Profiles Genomic

2

10

3000 30,000

4

New Sequencers 1 Human Genome in: 5 years (2001) 2 years (2004) 4 days (Jan 2008) 16 Hours (Oct 2008) 3 Hours (Nov 2009) 6 minutes (Now!)

Life is the transla-on of the informa-on in the genome into the phenotype of the

organism:

The organism ‚computes‘ this phenotype from its genotype, given a specific environment

(PentiumV) (neuronal net visualisation)

Genome

Phenotype

Organism = Computer Genome & the Environment

Slide Courtesy of Hans Lehrach






•  Conclusions

•  The Virtual Physiological Human is a methodological and technological descriptive, integrative and predictive, framework that is intended to enable the investigation of the human body as a single complex system

•  Aims •  Enable collaborative investigation of

the human body across all relevant scales

•  Introduce multiscale methodologies into medical and clinical research

Organism Organ Tissue

Cell Organelle Interaction

Protein Cell

Signals Transcript

Gene Molecule

€207M initiative in EU-FP7

What is the VPH?

…pa-ent-‐tailored computer models, used for diagnosis, preven-on, drug treatment and surgical planning – assess treatment before administering

Modelling how the human body works

Slide Courtesy of S. Kashif Sadiq

Environment

Population

dimensional scales

temporal scales Organism

Organ System

Organ

Tissue

Cell

Molecule

Atom

IntegraLon across..

organ systems






•  Conclusions

GENIUS: Grid Enabled Neurosurgical Imaging Using SimulaLon

The GENIUS project aims to model large scale pa-ent specific cerebral

blood flow in clinically relevant -me frames

ObjecLves: To study cerebral blood flow using paLent-‐specific image-‐based models To provide insights into the cerebral blood flow & anomalies To develop tools and policies by means of which users can be[er exploit the ability to reserve and co-‐reserve HPC resources To develop interfaces which permit users to easily deploy and monitor simula-ons across mul-ple computa-onal resources To visualize and steer the results of distributed simula-ons in real -me

Clinical SupercompuLng: Diagnosis and Decision Support in Surgery

•  Provide simula-on support from within the opera:ng theatre for neuroradiologists

•  Provide new informa.on to surgeons for pa.ent management and therapy: Diagnosis and risk assessment Predic-ve simula-on in therapy

•  Provide pa-ent-‐specific informa-on which can help plan embolisa-on of arterio-‐venous malforma-ons, coiling of aneurysms, etc.

GENIUS Clinical Workflow

Book compu-ng resources in advance or have a system by which simula-ons can be run urgently.

Shi^ imaging data around quickly over high-‐bandwidth low-‐latency dedicated links.

Interac-ve simula-ons and real-‐-me visualisa-on for immediate feedback.

15-20 minute turnaround

HIV-‐1 Protease is a common target for HIV drug therapy

•  Enzyme of HIV responsible for protein matura-on

•  Target for An--‐retroviral Inhibitors •  Example of Structure Assisted Drug

Design •  9 FDA inhibitors of HIV-‐1 protease

So what’s the problem? •  Emergence of drug resistant

muta-ons in protease •  Render drug ineffec-ve •  Drug resistant mutants have emerged

for all FDA inhibitors

Monomer B 101 - 199

Monomer A 1 - 99

Flaps

Leucine - 90, 190

Glycine - 48, 148

Catalytic Aspartic Acids - 25, 125

Saquinavir

P2 Subsite

N-terminal C-terminal

EU FP6 ViroLab project and EU FP7 CHAIN project

PaLent-‐specific HIV Drug Therapy

agtgttaccgtactcatcagactcgaggttcaccgtactcatcagactcgaattcaccgtactcatcagactcgattcaccgtactcatcagactcgsattcaaacccttggatcaagtgttaccgtactcatcagactcgsattcaccgtactcatcagactcgattcaccgtactcatcagactcgsattcaccgtactcatcagactcgdsaddttcaaaccgggtcacacaagg

Too many muta-ons to interpret by a clinician

Support so^ware is used to interpret genotypic assays from pa-ents

Uses both in vivo and in vitro data

Is dependent on Size and accuracy of in vivo clinical data set

Amount of in vitro phenotypic informa-on available -‐ e.g. binding affinity data

Simulator for Personalised Drug Ranking Simulator: a decision support software to assist clinicians for cancer treatment, and to reliably predicts patient-specific drug susceptibility.

Variant of target from patient

Array of available drugs

Simulator

Ranking of drug binding

The system could be used to rank proteins of different sequence with the same drug

Rapid and accurate prediction of binding free energies for saquinavir-bound HIV-1 proteases. Stoica I, Sadiq SK, Coveney PV. J Am Chem Soc. 2008 Feb 27;130(8):2639-48. Epub 2008 Jan 29.

The Life Science Problem

ExponenLal development of science, discovery, and engineering, yet

This does not seem to empower medicine! Promises without Delivery






•  Conclusions

Reference datasets Population view Open Data English Language Low legal involvement Trans-national

Research Clinic

Individual Patient Closed data National Language High level of legislation National Entities

RESEARCH MEDICINE

Slide Courtesy of Ewan Birney

Bioinformatics in biomedical research

(molecular, “omics”, systems biology)

Medical informatics In health care & clinical research

(EHR)

Translational Bioinformatics

Research re-use of clinical information

Linking Genotype

To Phenotype

Bridging gaps between BioinformaLcs and Medical InformaLcs

h[p://www.inbiomedvision.eu

Challenges ahead

Biological challenges –  Do we understand biology and

diseases enough to develop reliable computa-onal models?

–  How to integrate growing knowledge into models?

ICT Challenges –  Data quality –  Data management –  Data security –  User interfaces

Societal challenges –  Privacy –  How to prevent inequali-es in

access to health care? –  Health care economics –  Implementa-on in health care –  How to prevent adverse

effects/misuse?

secure management of the clinically-derived data across hospital-university interfaces, via development of large scale data integration warehouses, and back into clinical decision support systems

Data in hospitals

-‐ Medical imaging (MRI, CT, etc.) in various formats (JPEG, DICOM, .xls …)

-‐ Pseudonymised pa-ent informa-on (therapy details, follow-‐up diagnosis, treatments, etc.)

-‐ Genomic, DNA, RNA, protein/proteomics data, etc.

Medical data

Data integraLon & management •  How to store heterogeneous data in one environment? •  How to interface with the various types of data to understand and use?

(interoperability) •  How to deal with the large size of data resul-ng from complex

simula-ons, e.g. terabytes and petabytes?

•  How to acquire and transfer medical data from resource providers –  Burn anonymised data on CDs/

DVDs and pass them on to researchers vs electronic transfer from provider to data storage directly?

–  Network connecLvity for large simulaLons and data movements

•  Logis-cs –  IT infrastructure handling vast

amounts of data –  Availability of data in due Lme

–  Data storage/volume

–  Access to HPC

IMENSE: Individualised Medicine SimulaLon Environment •  Central integrated repository of pa-ent data for project clinicians &

researchers

–  Storage of and audit trail of computa-onal results –  Interfaces for data collec-on, edi-ng and display –  Provides a data environment for integra-on of mul--‐scale data &

decision support environment for clinicians

•  Cri-cal factors for Success and longevity –  Use Standards and Open Source solu-ons –  Use pre-‐exis-ng EU FP6/FP7 solu-ons and interac-on with VPH-‐

NoE Toolkit

S. J. Zasada et al., “IMENSE: An e-Infrastructure Environment for Patient Specific Multiscale Modelling and Treatment, Journal of Computational Science, In Press, Available online 26 July 2011, ISSN 1877-7503, DOI: 10.1016/j.jocs.2011.07.001.

Legal and ethical issues

Autonomy Well-‐being JusLce

Scien-sts Freedom to research

Facili-es and funding

Appropriate reward e.g. IP

Pa-ents Right to know or not to know

Improved treatment op-ons

Access to resources

Vulnerable groups Right to be heard Allevia-on of disadvantage

Equality

Professional groups

Professional judgment

Increased burden?

Implica-ons for prac-ce

Data breach is the unauthorised acquisi-on, access, use, or disclosure of protected health informa-on

ownership of data, compliance, what are the applicable laws and regula-ons

governing the data? Audi-ng in the cloud?

PaLent Empowerment






•  Conclusions

•  Exploit unprecedented amounts of detailed biological data being accumulated for individual people

•  Harness the latest developments in ICT

–  large scale data integra-on and mining, cloud compu-ng, high performance compu-ng, advanced modelling and simula-on,

–  all brought together in a highly flexible plajorm.

•  Turn this informa-on into knowledge that assists in taking medical, clinical and lifestyle decisions

IT Future of Medicine Up to €1B EU FET flagship proposal

h[p://www.ijom.eu

Medicine as driver of ICT innovaLon

Health care & society

User needs

Personalised medicine Public health

ITFoM Industry

ICT &

Biotech Pharma

Computational models of

biological systems: cells

organs individuals populations In

nova

tion

Virtual patient

Better drugs, disease prevention, evidence-based decision-making

A virtual paLent integraLon of models

Molecules

Tissues Anatomy

Statistics

35

ICT Layers of ITFoM






•  Conclusions

•  Data-‐intensive projects, and more future projects will be. –  biomedicine community is starving for storage;

–  network bandwidth now limi-ng: a faster network is needed for data movement.

•  Advanced IT allows us to analyse pa-ents all the way up from their own DNA sequences

•  A personalised approach is expected to lead to improved –  health outcomes

–  treatments

–  lifestyle choices for global ci-zens

Conclusions

Thank you for your a^enLon!

Nour Shublaq

Centre for Computa-onal Science University College London, UK

[email protected]

mie2012 27 aug12-shublaq

Technology